Prediabetes risk classification algorithm via carotid bodies and K-means clustering technique
Diabetes is a disease that affects millions of people in the world and its early screening prevents serious health problems, also providing relief in the demand for healthcare services. In the search for methods to support early diagnosis, this article introduces a novel prediabetes risk classificat...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2516.pdf |
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| author | Rafael F. Pinheiro Maria P. Guarino Marlene Lages Rui Fonseca-Pinto |
| author_facet | Rafael F. Pinheiro Maria P. Guarino Marlene Lages Rui Fonseca-Pinto |
| author_sort | Rafael F. Pinheiro |
| collection | DOAJ |
| description | Diabetes is a disease that affects millions of people in the world and its early screening prevents serious health problems, also providing relief in the demand for healthcare services. In the search for methods to support early diagnosis, this article introduces a novel prediabetes risk classification algorithm (PRCA) for type-2 diabetes mellitus (T2DM), utilizing the chemosensitivity of carotid bodies (CB) and K-means clustering technique from the field of machine learning. Heart rate (HR) and respiratory rate (RR) data from eight volunteers with prediabetes and 25 without prediabetes were analyzed. Data were collected in basal conditions and after stimulation of the CBs by inhalation of 100% of oxygen and after ingestion of a standardized meal. During the analysis, a greater variability of groups was observed in people with prediabetes compared to the control group, particularly after inhalation of oxygen. The algorithm developed from these results showed an accuracy of 86% in classifying for prediabetes. This approach, centered on CB chemosensitivity deregulation in early disease stages, offers a nuanced detection method beyond conventional techniques. Moreover, the adaptable algorithm and clustering methodology hold promise as risk classifications for other diseases. Future endeavors aim to validate the algorithm through longitudinal studies tracking disease development among volunteers and expand the study’s scope to include a larger participant pool. |
| format | Article |
| id | doaj-art-2fb3c45c59f542bda9b64438fe434e1c |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-2fb3c45c59f542bda9b64438fe434e1c2025-01-22T15:05:12ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e251610.7717/peerj-cs.2516Prediabetes risk classification algorithm via carotid bodies and K-means clustering techniqueRafael F. PinheiroMaria P. GuarinoMarlene LagesRui Fonseca-PintoDiabetes is a disease that affects millions of people in the world and its early screening prevents serious health problems, also providing relief in the demand for healthcare services. In the search for methods to support early diagnosis, this article introduces a novel prediabetes risk classification algorithm (PRCA) for type-2 diabetes mellitus (T2DM), utilizing the chemosensitivity of carotid bodies (CB) and K-means clustering technique from the field of machine learning. Heart rate (HR) and respiratory rate (RR) data from eight volunteers with prediabetes and 25 without prediabetes were analyzed. Data were collected in basal conditions and after stimulation of the CBs by inhalation of 100% of oxygen and after ingestion of a standardized meal. During the analysis, a greater variability of groups was observed in people with prediabetes compared to the control group, particularly after inhalation of oxygen. The algorithm developed from these results showed an accuracy of 86% in classifying for prediabetes. This approach, centered on CB chemosensitivity deregulation in early disease stages, offers a nuanced detection method beyond conventional techniques. Moreover, the adaptable algorithm and clustering methodology hold promise as risk classifications for other diseases. Future endeavors aim to validate the algorithm through longitudinal studies tracking disease development among volunteers and expand the study’s scope to include a larger participant pool.https://peerj.com/articles/cs-2516.pdfCarotid bodiesCBmeterK-meansMachine learningDiabetes |
| spellingShingle | Rafael F. Pinheiro Maria P. Guarino Marlene Lages Rui Fonseca-Pinto Prediabetes risk classification algorithm via carotid bodies and K-means clustering technique PeerJ Computer Science Carotid bodies CBmeter K-means Machine learning Diabetes |
| title | Prediabetes risk classification algorithm via carotid bodies and K-means clustering technique |
| title_full | Prediabetes risk classification algorithm via carotid bodies and K-means clustering technique |
| title_fullStr | Prediabetes risk classification algorithm via carotid bodies and K-means clustering technique |
| title_full_unstemmed | Prediabetes risk classification algorithm via carotid bodies and K-means clustering technique |
| title_short | Prediabetes risk classification algorithm via carotid bodies and K-means clustering technique |
| title_sort | prediabetes risk classification algorithm via carotid bodies and k means clustering technique |
| topic | Carotid bodies CBmeter K-means Machine learning Diabetes |
| url | https://peerj.com/articles/cs-2516.pdf |
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